MinThu11/burmese-disaster-classifier

Burmese Disaster Social Media Classifier

Fine-tuned xlm-roberta-base for classifying Burmese (Myanmar) disaster-related social media posts into four actionable categories. Useful for disaster-response triage and monitoring.

Labels

Label	Meaning
Immediate_Rescue_Needed	Posts requesting urgent rescue / help
Donation_Campaign	Posts offering or requesting donations & aid
General_News	News, warnings, and situational updates
Well_Wishing_Prayer	Prayers and well-wishing messages

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="MinThu11/burmese-disaster-classifier")
print(clf("ကလေးတွေရော အဘိုးကြီးရော ရေခေါင်မိုးထိတက်လာလို့ ပိတ်မိနေပါတယ် အမြန်လာကယ်ပေးကြပါ"))

Or load directly:

import torch, torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "MinThu11/burmese-disaster-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

inputs = tokenizer("...", return_tensors="pt", truncation=True, max_length=128)
with torch.no_grad():
    probs = F.softmax(model(**inputs).logits, dim=-1)[0]
print(model.config.id2label[int(probs.argmax())])

Training details

• Base model: xlm-roberta-base (multilingual encoder)
• Dataset: Myanmar disaster social media dataset (~1,000 posts, 80/20 train/test split)
• Epochs: 4
• Learning rate: 2e-5
• Batch size: 8
• Weight decay: 0.01
• Max sequence length: 128

Limitations

• Trained on a relatively small dataset (~1,000 examples); may not generalize to all disaster types or writing styles.
• Burmese-only; performance on other languages is not guaranteed.